Scene-Graph Learning for Ambiguous Spatial Instructions
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Solution Overview
Problem
Existing technologies struggle to accurately interpret ambiguous spatial instructions, such as 'right side' or 'left side', leading to difficulties in guiding a moving body to the intended area due to the lack of unique coordinate representation.
Innovation Solution
A learning device generates a pre-trained model using scene graphs to associate user instructions with location information, environment images, and spatial relationships, enabling it to output appropriate area candidates for achieving designated states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ambiguous spatial instructions like 'right side' are used in user instructions, then the instruction is easier to understand for users, but the precision of location identification deteriorates
Solution Approach 1:
The patent introduces scene graphs as an intermediary representation that bridges the gap between ambiguous natural language instructions and precise spatial coordinates. The scene graph captures semantic relationships between objects and spaces, enabling the system to interpret vague terms like 'right side' by analyzing the spatial context and relationships depicted in the scene graph, thus resolving the contradiction between ease of instruction and location precision
Solution Approach 2:
The system transforms the instruction interpretation from direct coordinate mapping to a multi-parameter analysis involving scene graph elements, spatial relationships, and contextual information. By changing the parameters considered in instruction interpretation (from simple directional vectors to comprehensive scene understanding), the system can handle ambiguous language while maintaining precise location identification
2Adaptability or versatility
If multiple area candidates are generated from ambiguous instructions, then the coverage of possible locations increases, but the complexity of selecting the correct area increases
Solution Approach 1:
The patent implements a feedback mechanism where the scene graph provides contextual information that feeds back into the area candidate selection process. The scene graph's representation of spatial relationships and object contexts serves as feedback to filter and rank area candidates, reducing the complexity of selection while maintaining comprehensive coverage of possible locations
Solution Approach 2:
The system generates multiple area candidates (excessive action) to ensure comprehensive coverage, but uses the scene graph to perform partial filtering by focusing on the most relevant spatial relationships and contextual cues, thus managing the complexity of selection without sacrificing coverage
Data Source
AI summary
Provided is a system capable of searching for appropriate area around a destination location for a moving body to realize a designated state in accordance with an instruction, by reflecting an instructor's intention underlying the instruction of ambiguous space designation with the destination location as reference. A pre-trained model is built using, as input data, scene graphs SG1 to SG3 created based on a user's instruction and an environment image in a direction toward a location of a moving body 20 and a designated place. The characteristic value of the primary node configuring the state scene graph SG1 is defined depending on the relative arrangement relationship (the distance and the angle) of each object with the location of the moving body 20 as a reference. The characteristic value of the primary node configuring the state scene graph SG1 is defined depending on a space occupancy mode of each object.